Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy

Inflammatory response signature score model for predicting immunotherapy response and pan-cancer prognosis

Background

Inflammatory responses influence the outcome of immunotherapy and tumorigenesis by modulating host immunity. However, systematic inflammatory response assessment models for predicting cancer immunotherapy (CIT) responses and survival across human cancers remain unexplored. Here, we investigated an inflammatory response score model to predict CIT responses and patient survival in a pan-cancer analysis.

Methods

We retrieved 12 CIT response gene expression datasets from the Gene Expression Omnibus database (GSE78220, GSE19423, GSE100797, GSE126044, GSE35640, GSE67501, GSE115821 and GSE168204), Tumor Immune Dysfunction and Exclusion database (PRJEB23709, PRJEB25780 and phs000452.v2.p1), European Genome-phenome Archive database (EGAD00001005738), and IMvigor210 cohort. The tumor samples from six cancers types: metastatic urothelial cancer, metastatic melanoma, gastric cancer, primary bladder cancer, renal cell carcinoma, and non-small cell lung cancer.We further established a binary classification model to predict CIT responses using the least absolute shrinkage and selection operator (LASSO) computational algorithm.

Findings

The model had high predictive accuracy in both the training and validation cohorts. During sub-group analysis, area under the curve (AUC) values of 0.82, 0.80, 0.71, 0.7, 0.67, and 0.64 were obtained for the non-small cell lung cancer, gastric cancer, metastatic urothelial cancer, primary bladder cancer, metastatic melanoma, and renal cell carcinoma cohorts, respectively. CIT response rates were higher in the high-scoring training cohort subjects (51%) than the low-scoring subjects (27%). The five-year survival rates in the high- and low score groups of the training cohorts were 62% and 21%, respectively, while those of the validation cohorts were 54% and 22%, respectively (P < 0·001 in all cases). Inflammatory response signature score derived from on-treatment tumor specimens are highly predictive of response to CIT in patients with metastatic melanoma. A significant correlation was observed between the inflammatory response scores and tumor purity. Regardless of the tumor purity, patients in the low score group had a significantly poorer prognosis than those in the high score group. Immune cell infiltration analysis indicated that in the high score cohort, tumor-infiltrating lymphocytes were significantly enriched, particularly effector and natural killer cells. Inflammatory response scores were positively correlated with immune checkpoint genes, suggesting that immune checkpoint inhibitors may have benefited patients with high scores. Analysis of signature scores across different cancer types from The Cancer Genome Atlas revealed that the prognostic performance of inflammatory response scores for survival in patients who have not undergone immunotherapy can be affected by tumor purity. Interleukin 21 (IL21) had the highest weight in the inflammatory response model, suggesting its vital role in the prediction mode. Since the number of metastatic melanoma patients (n = 429) was relatively large among CIT cohorts, we further performed a co-culture experiment using a melanoma cell line and CD8 + T cell populations generated from peripheral blood monocytes. The results showed that IL21 therapy combined with anti-PD1 (programmed cell death 1) antibodies (trepril monoclonal antibodies) significantly enhanced the cytotoxic activity of CD8 + T cells against the melanoma cell line.

Conclusion

In this study, we developed an inflammatory response gene signature model that predicts patient survival and immunotherapy response in multiple malignancies. We further found that the predictive performance in the non-small cell lung cancer and gastric cancer group had the highest value among the six different malignancy subgroups. When compared with existing signatures, the inflammatory response gene signature scores for on-treatment samples were more robust predictors of the response to CIT in metastatic melanoma.

Comprehensive genomic and spatial immune infiltration analysis of survival outliers in extensive-stage small cell lung cancer receiving first-line chemoimmunotherapy

BACKGROUND

A minority of patients with extensive-stage small cell lung cancer (ES-SCLC) exhibit prolonged survival following first-line chemoimmunotherapy, which warrants the use of reliable biomarkers. Here, we investigated the disparities in genomics and immune cell spatial distribution between long- and short-term survival of patients with ES-SCLC.

METHODS

We retrospectively recruited 11 long-term (>2 years) and 13 short-term (<9 months) ES-SCLC survivors receiving first-line chemoimmunotherapy. The samples were processed using targeted next-generation sequencing (tNGS), programmed death ligand-1 staining, multiplex immunohistochemical staining for immune cells (mIHC), tumor mutation burden (TMB), and chromosomal instability score measurements. The expression of putative genes in SCLC at the bulk and single-cell RNA-sequencing levels, as well as the role of putative genes in pan-cancer immunotherapy cohorts, were analyzed.

RESULTS

At the genomic level, a greater proportion of the smoking signature and higher TMB (>3.1) were associated with favorable survival. At the single-gene and pathway levels, tNGS revealed that MCL1 and STMN1 amplification and alterations in the apoptosis pathway were more common in short-term survivors, whereas alterations in the DLL3, KMT2B, HGF, EPHA3, ADGRB3, lysine deprivation, and HGF-cMET pathways were observed more frequently in long-term survivors. mIHC analysis of immune cells with different spatial distributions revealed that long-term survivors presented increased numbers of M1-like macrophages in all locations and decreased numbers of CD8+ T cells in the tumor stroma. Bulk transcriptomic analysis demonstrated that high levels of STMN1 and DLL3 represented an immune-suppressive tumor immune microenvironment (TIME), whereas HGF indicated an immune-responsive TIME. The expression levels of our putative genes were comparative in both TP53/RB1 mutant-type and TP53/RB1 wild-type. At the single-cell level, STMN1, MCL1, and DLL3 were highly expressed among all molecular subtypes (SCLC-A, SCLC-N, and SCLC-P), with STMN1 being enriched in cell division and G2M checkpoint pathways.

CONCLUSIONS

For ES-SCLC patients receiving first-line chemoimmunotherapy, alterations in DLL3, KMT2B, HGF, EPHA3, and ADGRB3 and a greater proportion of M1-like macrophages infiltration in all locations were predictors of favorable survival, while MCL1 and STMN1 amplification, as well as a greater proportion of CD8+ T cells infiltrating the tumor stroma, predicted worse survival.

Neoadjuvant vidutolimod and nivolumab in high-risk resectable melanoma: A prospective phase II trial

Intratumoral TLR9 agonists and anti-PD-1 produce clinical responses and broad immune activation. We conducted a single-arm study of neoadjuvant TLR9 agonist vidutolimod combined with anti-PD-1 nivolumab in high-risk resectable melanoma. In 31 evaluable patients, 55% major pathologic response (MPR) was observed, meeting primary endpoint. MPR was associated with necrosis, and melanophagocytosis with increased CD8+ tumor-infiltrating lymphocytes and plasmacytoid dendritic cells (pDCs) in the tumor microenvironment, and increased frequencies of Ki67+CD8+ T cells peripherally. MPRs had an enriched pre-treatment gene signature of myeloid cells, and response to therapy was associated with gene signatures of immune cells, pDCs, phagocytosis, and macrophage activation. MPRs gut microbiota were enriched for Gram-negative bacteria belonging to the Bacteroidaceae and Enterobacteriaceae families and the small subgroup of Gram-negative Firmicutes. Our findings support that combined vidutolimod and nivolumab stimulates a broad anti-tumor immune response and is associated with distinct baseline myeloid gene signature and gut microbiota. ClinicalTrials.gov identifier: NCT03618641.

Navigating the immune landscape with plasma cells: A pan-cancer signature for precision immunotherapy

Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan-cancer single-cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low-risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high-risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker-guided immunotherapy approaches in oncology.

A comprehensive investigation of associations between cell death pathways and molecular and clinical features in pan-cancer

BACKGROUND

Regulated cell death (RCD) pathways play significant roles in tumorigenesis. However, systematic investigation into correlations between RCD and various molecular and clinical features, particularly anti-tumor immunity and immunotherapy response in pan-cancer remains lacking.

METHODS

Using the single-sample gene set enrichment analysis, we quantified the activities of six RCD pathways (apoptosis, autophagy, ferroptosis, cuproptosis, necroptosis, and pyroptosis) in each cancer specimen. Then, we explored associations of these six RCD pathways with tumor immunity, genomic instability, tumor phenotypes and clinical features, and responses to immunotherapy and targeted therapies in pan-cancer by statistical analyses.

RESULTS

Our results showed that the RCD (except autophagy) activities were oncogenic signatures, as evidenced by their hyperactivation in late stage or metastatic cancer patients, positive correlations with tumor proliferation, stemness, genomic instability and intratumor heterogeneity, and correlation with worse survival outcomes in cancer. In contrast, autophagy was a tumor suppressive signature as its associations with molecular and clinical features in cancer shows an opposite pattern compared to the other RCD pathways. Furthermore, heightened RCD (except cuproptosis) activities were correlated with increased sensitivity to immune checkpoint inhibitors. Additionally, elevated activities of pyroptosis, autophagy, cuproptosis and necroptosis were associated with increased drug sensitivity in a broad spectrum of anti-tumor targeted therapies, while the elevated activity of ferroptosis was correlated with decreased sensitivity to numerous targeted therapies.

CONCLUSION

RCD (except autophagy) activities correlate with unfavorable cancer prognosis, while the autophagy activity correlate with favorable clinical outcomes. RCD (except cuproptosis) activities are positive biomarkers for anti-tumor immunity and immunotherapy response.

Lnc NEAT1 facilitates the progression of melanoma by targeting the miR-152-3p/CDK6 axis: An observational study

Long noncoding (Lnc) RNAs are novel regulators in melanoma. Lnc nuclear enriched autosomal transcript 1 (NEAT1) was reportedly upregulated in melanoma; however, the functional roles and mechanisms of Lnc NEAT1 need further investigation. Therefore, we used quantitative real-time PCR to determine the mRNA levels of Lnc NEAT1, miR-152-3p, and cyclin-dependent protein kinase 6 (CDK6). The protein level of CDK6 was determined by Western blot. Cell counting kit 8 and colony formation assays were used to assess cell proliferation. Cell migration was measured by wound healing and Transwell assays. Direct binding of the indicated molecules was verified by an RNA-binding protein immunoprecipitation assay and a dual luciferase reporter assay. The results revealed that Lnc NEAT1 and CDK6 were elevated, while miR-152-3p was downregulated in melanoma. Furthermore, Lnc NEAT1 was positively correlated with CDK6 expression and negatively correlated with miR-152-3p level. Furthermore, Lnc NEAT1 facilitated proliferation, migration, and invasion of melanoma cells. The underlying mechanism is that Lnc NEAT1 serves as a sponge for miR-152-3p to suppress the inhibitory effect of miR-152-3p on CDK6. Furthermore, the miR-152-3p/ CDK6 axis was implicated in the progression of melanoma accelerated by Lnc NEAT1. Taken together, Lnc NEAT1 may promote melanoma development by serving as an endogenous sponge of miR-152-3p, increasing CDK6 expression, and identifying a new target for the treatment of melanoma.

The classification of melanocytic gene signatures

Gene expression profiling technologies have revolutionized cell biology, enabling researchers to identify gene signatures linked to various biological attributes of melanomas, such as pigmentation status, differentiation state, proliferative versus invasive capacity, and disease progression. Although the discovery of gene signatures has significantly enhanced our understanding of melanocytic phenotypes, reconciling the numerous signatures reported across independent studies and different profiling platforms remains a challenge. Current methods for classifying melanocytic gene signatures depend on exact gene overlap and comparison with unstandardized baseline transcriptomes. In this study, we aimed to categorize published gene signatures into clusters based on their similar patterns of expression across clinical cutaneous melanoma specimens. We analyzed nearly 800 melanoma samples from six gene expression repositories and developed a classification framework for gene signatures that is resilient against biases in gene identification across profiling platforms and inconsistencies in baseline standards. Using 39 frequently cited published gene signatures, our analysis revealed seven principal classes of gene signatures that correlate with previously identified phenotypes: Differentiated, Mitotic/MYC, AXL, Amelanotic, Neuro, Hypometabolic, and Invasive. Each class is consistent with the phenotypes that the constituent gene signatures represent, and our classification method does not rely on overlapping genes between signatures. To facilitate broader application, we created WIMMS (what is my melanocytic signature, available at https://wimms.tanlab.org/), a user-friendly web application. WIMMS allows users to categorize any gene signature, determining its relationship to predominantly cited signatures and its representation within the seven principal classes.

Cell Identity and Spatial Distribution of PD-1/PD-L1 Blockade Responders

The programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) axis inhibits T cell activity, impairing anti-tumor immunity. Blocking this axis with therapeutic antibodies is one of the most promising anti-tumor immunotherapies. It has long been recognized that PD-1/PD-L1 blockade reinvigorates exhausted T (TEX) cells already present in the tumor microenvironment (TME). However, recent advancements in high-throughput gene sequencing and bioinformatic tools have provided researchers with a more granular and dynamic insight into PD-1/PD-L1 blockade-responding cells, extending beyond the TME and TEX populations. This review provides an update on the cell identity, spatial distribution, and treatment-induced spatiotemporal dynamics of PD-1/PD-L1 blockade responders. It also provides a synopsis of preliminary reports of potential PD-1/PD-L1 blockade responders other than T cells to depict a panoramic picture. Important questions to answer in further studies and the translational and clinical potential of the evolving understandings are also discussed.

Fatty acid metabolism constrains Th9 cell differentiation and antitumor immunity via the modulation of retinoic acid receptor signaling

T helper 9 (Th9) cells are interleukin 9 (IL-9)-producing cells that have diverse functions ranging from antitumor immune responses to allergic inflammation. Th9 cells differentiate from naïve CD4+ T cells in the presence of IL-4 and transforming growth factor-beta (TGF-β); however, our understanding of the molecular basis of their differentiation remains incomplete. Previously, we reported that the differentiation of another subset of TGF-β-driven T helper cells, Th17 cells, is highly dependent on de novo lipid biosynthesis. On the basis of these findings, we hypothesized that lipid metabolism may also be important for Th9 cell differentiation. We therefore investigated the differentiation and function of mouse and human Th9 cells in vitro under conditions of pharmacologically or genetically induced deficiency of the intracellular fatty acid content and in vivo in mice genetically deficient in acetyl-CoA carboxylase 1 (ACC1), an important enzyme for fatty acid biosynthesis. Both the inhibition of de novo fatty acid biosynthesis and the deprivation of environmental lipids augmented differentiation and IL-9 production in mouse and human Th9 cells. Mechanistic studies revealed that the increase in Th9 cell differentiation was mediated by the retinoic acid receptor and the TGF-β-SMAD signaling pathways. Upon adoptive transfer, ACC1-inhibited Th9 cells suppressed tumor growth in murine models of melanoma and adenocarcinoma. Together, our findings highlight a novel role of fatty acid metabolism in controlling the differentiation and in vivo functions of Th9 cells.

BET inhibition sensitizes innate checkpoint inhibitor resistant melanoma to anti-CTLA-4 treatment

Approximately 50% of melanoma patients fail to respond to immune checkpoint blockade (ICB), and acquired resistance hampers long-term survival in about half of initially responding patients. Whether targeting BET reader proteins, implicated in epigenetic dysregulation, can enhance ICB response rates and durability, remains to be determined. Here we show elevated BET proteins correlate with poor survival and ICB responses in melanoma patients. The BET inhibitor IBET151, combined with anti-CTLA-4, overcame innate ICB resistance however, sequential BET inhibition failed against acquired resistance in mouse models. Combination treatment response in the innate resistance model induced changes in tumor-infiltrating immune cells, reducing myeloid-derived suppressor cells (MDSCs). CD4+ and CD8+ T cells showed decreased expression of inhibitory receptors, with reduced TIM3, LAG3, and BTLA checkpoint expression. In human PBMCs in vitro, BET inhibition reduced expression of immune checkpoints in CD4+ and CD8+ T cells, restoring effector cytokines and downregulating the transcriptional driver TOX. BET proteins in melanoma may play an oncogenic role by inducing immune suppression and driving T cell dysfunction. The study demonstrates an effective combination for innately unresponsive melanoma patients to checkpoint inhibitor immunotherapy, yet highlights BET inhibitors' limitations in an acquired resistance context.

Lnc-H19-derived protein shapes the immunosuppressive microenvironment of glioblastoma

The immunosuppressive tumor microenvironment (TME) is a prominent feature of glioblastoma (GBM), the most lethal primary brain cancer resistant to current immunotherapies. The mechanisms underlying GBM-TME remain to be explored. We report that long non-coding RNA (LncRNA) H19 encodes an immune-related protein called H19-IRP. Functionally separated from H19 RNA, H19-IRP promotes GBM immunosuppression by binding to the CCL2 and Galectin-9 promoters and activating their transcription, thereby recruiting myeloid-derived suppressor cells (MDSCs) and tumor-associated macrophages (TAMs), leading to T cell exhaustion and an immunosuppressive GBM-TME. H19-IRP, overexpressed in clinical GBM samples, acts as a tumor-associated antigen (TAA) presented by major histocompatibility complex class I (MHC-I). A circular RNA vaccine targeting H19-IRP (circH19-vac) triggers a potent cytotoxic T cell response against GBM and inhibits GBM growth. Our results highlight the unrevealed function of H19-IRP in creating immunosuppressive GBM-TME by recruiting MDSCs and TAMs, supporting the idea of targeting H19-IRP with cancer vaccine for GBM treatment.

A regularized Cox hierarchical model for incorporating annotation information in predictive omic studies

BACKGROUND

Associated with high-dimensional omics data there are often "meta-features" such as biological pathways and functional annotations, summary statistics from similar studies that can be informative for predicting an outcome of interest. We introduce a regularized hierarchical framework for integrating meta-features, with the goal of improving prediction and feature selection performance with time-to-event outcomes.

METHODS

A hierarchical framework is deployed to incorporate meta-features. Regularization is applied to the omic features as well as the meta-features so that high-dimensional data can be handled at both levels. The proposed hierarchical Cox model can be efficiently fitted by a combination of iterative reweighted least squares and cyclic coordinate descent.

RESULTS

In a simulation study we show that when the external meta-features are informative, the regularized hierarchical model can substantially improve prediction performance over standard regularized Cox regression. We illustrate the proposed model with applications to breast cancer and melanoma survival based on gene expression profiles, which show the improvement in prediction performance by applying meta-features, as well as the discovery of important omic feature sets with sparse regularization at meta-feature level.

CONCLUSIONS

The proposed hierarchical regularized regression model enables integration of external meta-feature information directly into the modeling process for time-to-event outcomes, improves prediction performance when the external meta-feature data is informative. Importantly, when the external meta-features are uninformative, the prediction performance based on the regularized hierarchical model is on par with standard regularized Cox regression, indicating robustness of the framework. In addition to developing predictive signatures, the model can also be deployed in discovery applications where the main goal is to identify important features associated with the outcome rather than developing a predictive model.

Comprehensive single-cell and bulk transcriptomic analyses to develop an NK cell-derived gene signature for prognostic assessment and precision medicine in breast cancer

Background

Natural killer (NK) cells play crucial roles in mediating anti-cancer activity in breast cancer (BRCA). However, the potential of NK cell-related molecules in predicting BRCA outcomes and guiding personalized therapy remains largely unexplored. This study focused on developing a prognostic and therapeutic prediction model for BRCA by incorporating NK cell-related genes.

Methods

The data analyzed primarily originated from the TCGA and GEO databases. The prognostic role of NK cells was evaluated, and marker genes of NK cells were identified via single-cell analysis. Module genes closely associated with immunotherapy resistance were identified by bulk transcriptome-based weighted correlation network analysis (WGCNA). Following taking intersection and LASSO regression, NK-related genes (NKRGs) relevant to BRCA prognosis were screened, and the NK-related prognostic signature was subsequently constructed. Analyses were further expanded to clinicopathological relevance, GSEA, tumor microenvironment (TME) analysis, immune function, immunotherapy responsiveness, and chemotherapeutics. Key NKRGs were screened by machine learning and validated by spatial transcriptomics (ST) and immunohistochemistry (IHC).

Results

Tumor-infiltrating NK cells are a favorable prognostic factor in BRCA. By combining scRNA-seq and bulk transcriptomic analyses, we identified 7 NK-related prognostic NKRGs (CCL5, EFHD2, KLRB1, C1S, SOCS3, IRF1, and CCND2) and developed an NK-related risk scoring (NKRS) system. The prognostic reliability of NKRS was verified through survival and clinical relevance analyses across multiple cohorts. NKRS also demonstrated robust predictive power in various aspects, including TME landscape, immune functions, immunotherapy responses, and chemotherapeutic sensitivity. Additionally, KLRB1 and CCND2 emerged as key prognostic NKRGs identified through machine learning and external validation, with their expression correlation with NK cells confirmed in BRCA specimens by ST and IHC.

Conclusions

We developed a novel NK-related gene signature that has proven valuable for evaluating prognosis and treatment response in BRCA, expecting to advance precision medicine of BRCA.

Interferon-induced factor 16 is essential in metastatic melanoma to maintain STING levels and the immune responses upon IFN-γ response pathway activation

BACKGROUND

Immune checkpoint inhibitor (ICIs)-based therapies are the standard of care treatment for patients with metastatic melanoma (MM). The stimulator of interferon genes (STING) signaling pathway is critical in controlling immune responses to ICIs. Interferon (IFN)-γ-inducible protein 16 (IFI16) is a cytosolic DNA sensor that activates the STING signaling pathway. The link between IFI16 and STING signaling pathway on IFN-γ stimulation and the connection to ICIs response remains not completely understood.

METHODS

Deconvolution analyses were performed using the TCGA-SKCM, GSE91061, and PRJEB23709 public RNA sequencing (RNA-seq) data sets that contained RNA-seq for patients with MM. Functional assays combined with cytokine arrays were performed using MM cell lines to validate in silico data. Multiplex immunofluorescence was performed on untreated or pretreatment tumor samples from patients with MM.

RESULTS

Deconvolution analysis showed that high-IFI16 levels in melanoma cells were associated with a good prognosis in patients with MM and positively correlated with M1-macrophage infiltration. Functional assays using MM cell lines demonstrated that IFI16 is a key molecule to sense cytosolic DNA and activate STING and nuclear factor kappa B (NF-κB) signaling pathways, independent of cyclic GMP-AMP synthase or absent in melanoma 2, on IFN-γ stimulation. IFI16 knockdown significantly decreased CXCL10 and ICAM1 secretion. EZH2 inhibitor reversed the repressive epigenetic control on IFI16 to promote STING and NF-κB signaling pathways on IFN-γ stimulation. Increased IFI16, ICAM1, and CXCL10 levels in tumor samples from patients with MM were positively correlated with M1-macrophage infiltration and a significantly better response to ICIs.

CONCLUSIONS

This study identifies IFI16 as a key sensor during IFN-γ stimulation associated with ICI response, and it proposes the epigenetic EZH2 inhibitor as an alternative treatment strategy to overcome ICI resistance in patients with MM.

Impact of senescence cell signature in patients with non-small cell carcinoma and melanoma receiving PD-L1/PD-1 inhibitors

Tumor cell senescence plays a crucial role in tumor immunity. We investigated whether the senescent cell signature (SCS) could predict prognosis in non-small cell carcinoma (NSCLC) and melanoma datasets treated with PD-L1/PD-1 inhibitors. Patients with high SCS expression exhibited elevated levels of interferon-gamma and T cell-inflamed signatures in three lung adenocarcinomas (LUAD), two squamous cell carcinoma (LUSC) and three melanoma datasets. The high SCS group was associated with PD-L1-related pathways such as IL6/JAK/STAT3 and TNF-alpha signaling via NF-kB in LUAD, LUSC, and melanoma datasets. A positive correlation was observed between several immune checkpoint markers and the SCS, indicating an immunosuppressive state in LUAD, LUSC and melanoma datasets. In patients treated with PD-1/PD-L1 inhibitors, a higher SCS was associated with a better prognosis, and a positive correlation between SCS and PD-L1 was observed in six independent NSCLC and three independent melanoma datasets. We used the LASSO Cox regression model to build a risk model focusing on the SCS genes that particularly predict prognosis. We confirmed that the model accurately predicts prognosis. However, the senescent immunohistochemical markers p16 and p21 could predict the response to PD-1/PD-L1 inhibitors in patients with LUSC and melanoma but not in patients with LUAD. SCS could serve as a valuable biomarker to complement PD-L1 expression in patients receiving PD-L1/PD-1 inhibitors.

Comprehensive Characterization of the Integrin Family Across 32 Cancer Types

Integrin genes are widely involved in tumorigenesis. Yet, a comprehensive characterization of integrin family members and their interactome at the pan-cancer level is lacking. Here, we systematically analyzed integrin family in approximately 10,000 tumors across 32 cancer types. Globally, integrins represent a frequently altered and misexpressed pathway, with alteration and dysregulation overall being protumorigenic. Expression dysregulation, better than mutational landscape, of integrin family successfully identifies a subgroup of aggressive tumors with a high level of proliferation and stemness. The results reveal that several molecular mechanisms collectively regulate integrin expression in a context-dependent manner. For potential clinical usage, we constructed a weighted scoring system, integrinScore, to measure integrin signaling patterns in individual tumors. Remarkably, integrinScore was consistently correlated with predefined molecular subtypes in multiple cancers, with integrinScore-high tumors being more aggressive. Importantly, integrinScore was cancer-dependent and closely associated with proliferation, stemness, tumor microenvironment, metastasis, and immune signatures. IntegrinScore also predicted patients' response to immunotherapy. By mining drug databases, we unraveled an array of compounds that may modulate integrin signaling. Finally, we built a user-friendly database, Pan-cancer Integrin Explorer (PIExplorer; http://computationalbiology.cn/PIExplorer), to facilitate researchers to explore integrin-related knowledge. Collectively, we provide a comprehensive characterization of integrins across cancers and offer gene-specific and cancer-specific rationales for developing integrin-targeted therapy.

Pan-cancer γδ TCR analysis uncovers clonotype diversity and prognostic potential

Gamma-delta T cells (γδ T cells) play a crucial role in both innate and adaptive immunity within tumors, yet their presence and prognostic value in cancer remain underexplored. This study presents a large-scale analysis of γδ T cell receptor (γδ TCR) reads from 11,000 tumor samples spanning 33 cancer types, utilizing the TRUST4 algorithm. Our findings reveal extensive diversity in γδ TCR clonality and gene expression, underscoring the potential of γδ T cells as prognostic biomarkers in various cancers. We further demonstrate the utility of TCR gamma (TRG) and delta (TRD) gene expression from standard RNA-sequencing (RNA-seq) data. This comprehensive dataset offers a valuable resource for advancing γδ T cell research, with implications for enhanced immunotherapy approaches or alternative therapeutic strategies. Additionally, our centralized database supports translational research into the therapeutic significance of γδ T cells.

Prognostic value of TMTC1 in pan-cancer analysis

Background

Transmembrane and tetratricopeptide repeat containing 1 (TMTC1) is a recently discovered enzyme involved in the O-mannosylation of cadherins and protocadherins. It has been implicated in various types of cancer, but the overall prognostic significance of TMTC1 in pan-cancer and its potential as an immunotherapeutic target remain unclear.

Methods

We applied various bioinformatics methods to investigate the potential oncogenic roles of TMTC1 using public databases. This analysis involved examining the expression, prognosis, genetic alterations, immune infiltration, immunotherapy response, drug sensitivity, and regulatory mechanisms of the TMTC1 gene in diverse cancer types.

Results

In this study, we observed that TMTC1 expression is reduced in 19 types of cancer (ACC, BLCA, BRCA, CESC, COAD, ESCA, GBM, KICH, KIRC, KIRP, LAML, LUAD, LUSC, PRAD, READ, STAD, THCA, UCEC, and UCS) compared to normal tissues. Conversely, TMTC1 expression is elevated in OV and PAAD relative to normal tissues. Moreover, our analysis revealed that high expression of TMTC1 was associated with worse overall survival (OS) outcomes in patients with ACC, BLCA, COAD, GBM, KIRP, OV, STAD, and UCEC, but better OS outcomes in patients with CESC, KIRC, LUSC, and PAAD. Notably, patients with TMTC1 mutations or deep deletions demonstrated longer OS, while those with TMTC1 amplification showed shorter OS. There was a significant correlation between the expression level of TMTC1 and the infiltration of cancer-associated fibroblasts (CAFs) and endothelial cells. Using data from six real-world immunotherapy cohorts of BLCA, SKCM and RCC, we discovered that high TMTC1 expression was associated with better OS or progression-free survival (PFS). Lastly, through TMTC1-related gene enrichment analysis, some biological processes and pathways were found to be significantly enriched, such as vascular endothelial growth factor receptor signaling pathway and ECM-receptor interaction.

Conclusions

Our study demonstrates the prognostic significance of TMTC1 in pan-cancer and highlights its potential as an immunotherapeutic target.

A machine learning model reveals expansive downregulation of ligand-receptor interactions that enhance lymphocyte infiltration in melanoma with developed resistance to immune checkpoint blockade

Immune checkpoint blockade (ICB) is a promising cancer therapy; however, resistance frequently develops. To explore ICB resistance mechanisms, we develop Immunotherapy Resistance cell-cell Interaction Scanner (IRIS), a machine learning model aimed at identifying cell-type-specific tumor microenvironment ligand-receptor interactions relevant to ICB resistance. Applying IRIS to deconvolved transcriptomics data of the five largest melanoma ICB cohorts, we identify specific downregulated interactions, termed resistance downregulated interactions (RDI), as tumors develop resistance. These RDIs often involve chemokine signaling and offer a stronger predictive signal for ICB response compared to upregulated interactions or the state-of-the-art published transcriptomics biomarkers. Validation across multiple independent melanoma patient cohorts and modalities confirms that RDI activity is associated with CD8 + T cell infiltration and highly manifested in hot/brisk tumors. This study presents a strongly predictive ICB response biomarker, highlighting the key role of downregulating chemotaxis-associated ligand-receptor interactions in inhibiting lymphocyte infiltration in resistant tumors.

Mechanisms of HIF1A-mediated immune evasion in gastric cancer and the impact on therapy resistance

BACKGROUND

The high prevalence and detrimental effects on patient outcomes make gastric cancer (GC) a significant health issue that persists internationally. Existing treatment modalities exhibit limited efficacy, prompting the exploration of immune checkpoint inhibitors as a novel therapeutic approach. However, resistance to immunotherapy poses a significant challenge in GC management, necessitating a profound grasp of the intrinsic molecular pathways.

METHODS

This study focuses on investigating the immunosuppressive mechanisms of quiescent cancer cells (QCCs) in GC, particularly their resistance to T-cell-mediated immune responses. Utilizing mouse models, gene editing techniques, and transcriptome sequencing, we aim to elucidate the interactions between QCCs, immune cells, and key regulatory factors like HIF1A. Functional enrichment analysis will further underscore the role of glycolysis-related genes in mediating immunosuppression by QCCs.

RESULTS

The cancer cells that survived GC treated with T-cell therapy lost their proliferative ability. QCCs, as the main resistance force to immunotherapy, exhibit stronger resistance to CD8+ T-cell attack and possess higher cancer-initiating potential. Single-cell sequencing analysis revealed that the microenvironment in the QCCs region harbors more M2-type tumor-associated macrophages and fewer T cells. This microenvironment in the QCCs region leads to the downregulation of T-cell immune activation and alters macrophage metabolic function. Transcriptome sequencing of QCCs identified upregulated genes related to chemo-resistance, hypoxia, and glycolysis. In vitro cell experiments illustrated that HIF1A promotes the transcription of glycolysis-related genes, and silencing HIF1A in QCCs enhances T-cell proliferation and activation in co-culture systems, induces apoptosis in QCCs, and increases QCCs' sensitivity to immune checkpoint inhibitors. In vivo, animal experiments showed that silencing HIF1A in QCCs can inhibit GC growth and metastasis.

CONCLUSION

Unraveling the molecular mechanisms by which QCCs resist T-cell-mediated immune responses through immunosuppression holds promising implications for refining treatment strategies and enhancing patient outcomes in GC. By delineating these intricate interactions, this study contributes crucial insights into precision medicine and improved therapeutic outcomes in GC management.

MerTK+ macrophages promote melanoma progression and immunotherapy resistance through AhR-ALKAL1 activation

Despite our increasing understanding of macrophage heterogeneity, drivers of macrophage phenotypic and functional polarization in the microenvironment are not fully elucidated. Here, our single-cell RNA sequencing data identify a subpopulation of macrophages expressing high levels of the phagocytic receptor MER proto-oncogene tyrosine kinase (MerTK+ macrophages), which is closely associated with melanoma progression and immunotherapy resistance. Adoptive transfer of the MerTK+ macrophages into recipient mice notably accelerated tumor growth regardless of macrophage depletion. Mechanistic studies further revealed that ALK And LTK Ligand 1 (ALKAL1), a target gene of aryl hydrocarbon receptor (AhR), facilitated MerTK phosphorylation, resulting in heightened phagocytic activity of MerTK+ macrophages and their subsequent polarization toward an immunosuppressive phenotype. Specifically targeted delivery of AhR antagonist to tumor-associated macrophages with mannosylated micelles could suppress MerTK expression and improved the therapeutic efficacy of anti-programmed cell death ligand 1 therapy. Our findings shed light on the regulatory mechanism of MerTK+ macrophages and provide strategies for improving the efficacy of melanoma immunotherapy.

Aberrant cytoplasmic expression of UHRF1 restrains the MHC-I-mediated anti-tumor immune response

Immunotherapy successfully complements traditional cancer treatment. However, primary and acquired resistance might limit efficacy. Reduced antigen presentation by MHC-I has been identified as potential resistance factor. Here we show that the epigenetic regulator ubiquitin-like with PHD and ring finger domains 1 (UHRF1), exhibits altered expression and aberrant cytosolic localization in cancerous tissues, where it promotes MHC-I ubiquitination and degradation. Cytoplasmic translocation of UHRF1 is induced by its phosphorylation on a specific serine in response to signals provided by factors present in the tumor microenvironment (TME), such as TGF-β, enabling UHRF1 to bind MHC-I. Downregulation of MHC-I results in suppression of the antigen presentation pathway to establish an immune hostile TME. UHRF1 inactivation by genetic deletion synergizes with immune checkpoint blockade (ICB) treatment and induces an anti-tumour memory response by evoking low-affinity T cells. Our study adds to the understanding of UHRF1 in cancer immune evasion and provides a potential target to synergize with immunotherapy and overcome immunotherapeutic resistance.

Immunogenic cell death signatures from on-treatment tumor specimens predict immune checkpoint therapy response in metastatic melanoma

Melanoma is a highly malignant form of skin cancer that typically originates from abnormal melanocytes. Despite significant advances in treating metastatic melanoma with immune checkpoint blockade (ICB) therapy, a substantial number of patients do not respond to this treatment and face risks of recurrence and metastasis. This study collected data from multiple datasets, including cohorts from Riaz et al., Gide et al., MGH, and Abril-Rodriguez et al., focusing on on-treatment samples during ICB therapy. We used the single-sample gene set enrichment analysis (ssGSEA) method to calculate immunogenic cell death scores (ICDS) and employed an elastic network algorithm to construct a model predicting ICB efficacy. By analyzing 18 ICD gene signatures, we identified 9 key ICD gene signatures that effectively predict ICB treatment response for on-treatment metastatic melanoma specimens. Results showed that patients with high ICD scores had significantly higher response rates to ICB therapy compared to those with low ICD scores. ROC analysis demonstrated that the AUC values for both the training and validation sets were around 0.8, indicating good predictive performance. Additionally, survival analysis revealed that patients with high ICD scores had longer progression-free survival (PFS). This study used an elastic network algorithm to identify 9 ICD gene signatures related to the immune response in metastatic melanoma. These gene features can not only predict the efficacy of ICB therapy but also provide references for clinical decision-making. The results indicate that ICD plays an important role in metastatic melanoma immunotherapy and that expressing ICD signatures can more accurately predict ICB treatment response and prognosis for on-treatment metastatic melanoma specimens, thus providing a basis for personalized treatment.

A Predictive Network-Based Immune Checkpoint Blockade Immunotherapeutic Signature Optimizing Patient Selection and Treatment Strategies

Immune checkpoint blockade (ICB) therapy has brought significant advancements to the field of oncology. However, the diverse responses among patients highlight the need for more accurate predictive tools. In this study, insights are drawn from tumor-immunology pathways, and a novel network-based ICB immunotherapeutic signature, termed ICBnetIS, is constructed. The signature is derived from advanced biological network-based computational strategies involving co-expression networks and molecular interactions networks. The efficacy of ICBnetIS is established through its association with enhanced patient survival and a robust immune response characterized by diverse immune cell infiltration and active anti-tumor immune pathways. The validation process positions ICBnetIS as an effective tool in predicting responses to ICB therapy, analyzing ICB data from a broad collection of over 700 samples from multiple cancer types of more than 15 datasets. It achieves an aggregated prediction AUC of 0.784, which outperforms the other nine renowned immunotherapeutic signatures, indicating the superior predictive capability of ICBnetIS. To sum up, the findings suggest ICBnetIS as a potent tool in predicting ICB therapy responses, offering significant implications for patient selection and treatment optimization in oncology. The study highlights the role of ICBnetIS in advancing personalized treatment strategies, potentially transforming the clinical landscape of ICB therapy.

Comprehensive analysis to identify IL7R as a immunotherapy biomarker from pan-cancer analysis to in vitro validation

BACKGROUND

Immunotherapy faces a major challenge in treatment resistance, highlighting the need for efficacy biomarkers identification. The tumor microenvironment (TME) significantly influences treatment outcomes, necessitating molecular TME exploration to address immunotherapy resistance.

METHODS

The study initially pinpointed IL7R as a pivotal TME gene and then examined its impact on TME's CD8 + T cells at the single-cell level. Bulk-RNA analysis investigated IL7R function, immune cell infiltration related to IL7R in TCGA pan-cancer samples with its expression verified in clinical samples through immunohistochemistry. Genome instability and immune-related molecular expression associated with IL7R were also assessed. Furthermore, the clinical efficacy of IL7R was evaluated in various immunotherapy treatment cohorts.

RESULTS

Our single-cell analyses and cell-cased experiment revealed that T cells with high IL7R expression tended to be non-terminal and correlated with favorable immunotherapy responses. High IL7R expression corresponded to increased immune and stromal cell signiture, immune pathway enrichment, and an immune-inflamed environment in Bulk-RNA analysis and immunohistochemistry verification. These patients exhibited higher proportions of memory T cells and M1 cells within the TME, along with frequent genome instability and immune molecular upregulation. While IL7R had varied prognostic impact across the TCGA dataset, patients with high IL7R expression showed extended survival under immunotherapy.

CONCLUSION

IL7R plays a critical role in shaping TME diversity across cancer types and holds promise as a relevant biomarker for predicting immunotherapy benefits.

MHC class II genotypes are independent predictors of anti-PD1 immunotherapy response in melanoma

BACKGROUND

Immune checkpoint blockade is a highly successful anti-cancer immunotherapy. Both CTLA4 and PD1 checkpoint blockers are clinically available for melanoma treatment, with anti-PD1 therapy reaching response rates of 35-40%. These responses, which are mediated via neoantigen presentation by the polymorphic MHC complex, are hard to predict and the tumor mutation burden is currently one of the few available biomarkers. While MHC genotypes are expected to determine therapy responses, association studies have remained largely elusive.

METHODS

We developed an overall MHC genotype binding score (MGBS), indicative of a patient's MHC class I (MHC-I) and class II (MHC-II) neoantigen binding capacity and solely based on the germline MHC-I (MGBS-I) and MHC-II (MGBS-II) genotypes. These scores were then correlated to survival and clinical responses following anti-PD1 immunotherapy in a previously published dataset of 144 melanoma patients.

RESULTS

We demonstrate that MGBS scores are TMB-independent predictors of anti-PD1 immunotherapy responses in melanoma. Opposite outcomes were found for both MHC classes, with high MGBS-I and MGBS-II predicting good and bad outcomes, respectively. Interestingly, high MGBS-II is mainly associated with treatment response failure in a subgroup of anti-CTLA4 pretreated patients.

CONCLUSIONS

Our results suggest that MGBS, calculated solely from the MHC genotype, has clinical potential as a non-invasive and tumor-independent biomarker to guide anti-cancer immunotherapy in melanoma.

Integrative analysis of pan-cancer single-cell data reveals a tumor ecosystem subtype predicting immunotherapy response

Tumor ecosystem shapes cancer biology and potentially influence the response to immunotherapy, but there is a lack of direct clinical evidence. In this study, we utilized EcoTyper and publicly available scRNA-Seq cohorts from ICI-treated patients. We found a ecosystem subtype (ecotype) was linked to improved responses to immunotherapy. Then, a novel immunotherapy-responsive ecotype signature (IRE.Sig) was established and validated through the analysis of pan-cancer data. Utilizing IRE.Sig, machine learning models successfully predicted ICI responses in both validation and testing cohorts, achieving area under the curve (AUC) values of 0.72 and 0.71, respectively. Furthermore, using 5 CRISPR screening cohorts, we identified several potential drugs that may augment the efficacy of ICI. We also elucidated the candidate cellular biomarkers of response to the combined treatment of pembrolizumab plus eribulin in breast cancer. This signature has the potential to serve as a valuable tool for patients in selecting appropriate immunotherapy treatments.

Neutrophil extracellular trap genes predict immunotherapy response in gastric cancer

Background

Neutrophil extracellular trap (NET) is associated with host response, tumorigenesis, and immune dysfunction. However, the link between NET and the tumor microenvironment (TME) of gastric cancer (GC) remains unclear. Our study aims to characterize the expression patterns of NET-related genes and their relationships with clinicopathological characteristics, prognosis, TME features, and immunotherapy efficacy in GC cohorts.

Methods

Transcriptomic and single-cell RNA sequencing profiles of GC with annotated clinicopathological data were obtained from TCGA-STAD (n = 415), GSE62254 (n = 300), GSE15459 (n = 192), and GSE183904 (n = 26). The consensus cluster algorithm was used to classify tumor samples into different NET-related clusters. A NET-related signature was constructed using LASSO regression and verified in four immunotherapy cohorts. ROC and Kaplan-Meier analyses were conducted to evaluate the predictive and prognostic value of the model for immunotherapy efficacy.

Results

This study identified two NET-related clusters with distinct clinicopathological features, prognosis, and TME landscapes. The high NET-related cluster, characterized by increased NET-related gene expression, exhibited more aggressive behavior and a worse prognosis (HR = 1.63, P = 0.004) than the low NET-related cluster. DEGs were primarily involved in the chemokine/cytokine-associated pathways. Moreover, the high NET-related cluster had significantly higher levels of TME scores, immune infiltration, and immune effectors (all P < 0.001). The NET-related signature displayed a high predictive accuracy for immunotherapy response (AUC = 0.939, P < 0.001). Furthermore, patients with high NET-related scores consistently harbored a more favorable prognosis in different immunotherapy cohorts (all P < 0.05).

Conclusions

This study identified the NET-related signature as a robust model for predicting immunotherapy response in GC, which can help clinicians make appropriate immunotherapy decisions.

Development and validation of a novel immune‒metabolic-Based classifier for hepatocellular carcinoma

The heterogeneity of immune cells and metabolic pathways in hepatocellular carcinoma (HCC) patients has not been fully elucidated, leading to diverse clinical outcomes. Accurately distinguishing different HCC subtypes and recommending appropriate treatments is are highly important. In this study, we conducted a comprehensive analysis of 28 immune cells and 85 metabolic pathways in the TCGA-LIHC and GSE14520 datasets. Metabolism-related first principal component (MRPC1) and cytotoxic T lymphocyte (CTL) infiltration were used to assess the metabolic and immune infiltration levels of HCC patients, respectively. These two quantifiable indicators were then used to construct an immune‒metabolic classifier, which categorized HCC patients into three distinct groups. The potential biological mechanisms were explored through multiomics analysis, revealing that group S1 exhibited high metabolic activity and a high level of immune infiltration, that group S2 presented a low level of immune infiltration, and that group S3 presented low metabolic activity. This new immune‒metabolic classifier was well validated in a pancancer cohort of 9296 patients. The efficacy of multiple treatment approaches was assessed in relation to different immune‒metabolic groups, indicating that group S1 patients may benefit from immunotherapy, that group S2 patients are suitable for transcatheter arterial chemoembolization (TACE), and that group S3 patients are appropriate candidates for tyrosine kinase inhibitors. In conclusion, this immune‒metabolic classifier is anticipated to address the differences in treatment efficacy among HCC patients due to the heterogeneity of the tumor microenvironment, and to help refine the individualized treatment choices for clinical patients.

Integrated multiomics revealed adenosine signaling predict immunotherapy response and regulate tumor ecosystem of melanoma

Extracellular adenosine is extensively involved in regulating the tumor microenvironment. Given the disappointing results of adenosine-targeted therapy trials, personalized treatment might be necessary, tailored to the microenvironment status of individual patients. Here, we introduce the adenosine signaling score (ADO-score) model using non-negative matrix fraction identified patient subtypes using publicly available melanoma dataset, which aimed to profile adenosine signaling-related genes and construct a model to predict prognosis. We analyzed 580 malignant melanoma samples and demonstrated its robust value for prognosis. Further investigation in immune checkpoint inhibitor dataset suggests its potential as a stratified factor of immune checkpoint inhibitor efficacy. We validated the power of the ADO-score at the protein level immunofluorescence in a melanoma cohort from Xiangya Hospital. More importantly, single-cell and spatial transcriptomic data highlighted the cell-specific expression patterns of adenosine signaling-related genes and the existence of adenosine signaling-mediated crosstalk between tumor cells and immune cells in melanoma. Our study reveals a robust connection between adenosine signaling and clinical benefits in melanoma patients and proposes a universally applicable adenosine signaling model, the ADO-score, in gene expression profiles and histological sections. This model enables us to more precisely and conveniently select patients who are likely to benefit from immunotherapy.

Regulatory T cells: masterminds of immune equilibrium and future therapeutic innovations

Regulatory T cells (Tregs), a subset of CD4+T cells marked by the expression of the transcription factor forkhead box protein 3 (Foxp3), are pivotal in maintaining immune equilibrium and preventing autoimmunity. In our review, we addressed the functional distinctions between Foxp3+Tregs and other T cells, highlighting their roles in autoimmune diseases and cancer. We uncovered the dual nature of Tregs: they prevented autoimmune diseases by maintaining self-tolerance while contributing to tumor evasion by suppressing anti-tumor immunity. This study underscored the potential for targeted therapeutic strategies, such as enhancing Treg activity to restore balance in autoimmune diseases or depleting Foxp3+Tregs to augment anti-tumor immune responses in cancer. These insights laid the groundwork for future research and clinical applications, emphasizing the critical role of Foxp3+Tregs in immune regulation and the advancement of next-generation immunotherapies.

Lymphocyte T Subsets and Outcome of Immune Checkpoint Inhibitors in Melanoma Patients: An Oncologist's Perspective on Current Knowledge

Melanoma is the most aggressive and deadly form of skin cancer, and its incidence has been steadily increasing over the past few decades, particularly in the Caucasian population. Immune checkpoint inhibitors (ICI), anti-PD-1 monotherapy or in combination with anti-CTLA-4, and more recently, anti-PD-1 plus anti-LAG-3 have changed the clinical evolution of this disease. However, a significant percentage of patients do not benefit from these therapies. Therefore, to improve patient selection, it is imperative to look for novel biomarkers. Immune subsets, particularly the quantification of lymphocyte T populations, could contribute to the identification of ICI responders. The main purpose of this review is to thoroughly examine significant published data on the potential role of lymphocyte T subset distribution in peripheral blood (PB) or intratumorally as prognostic and predictive of response biomarkers in advanced melanoma patients treated with ICI regardless of BRAFV600 mutational status.

Single-cell RNA Sequencing Analysis Reveals Cancer-associated Fibroblast Signature for Prediction of Clinical Outcomes and Immunotherapy in Gastric Cancer

Gastric cancer (GC) is a significant worldwide health concern and is a leading cause of cancer-related mortality. Immunotherapy has arisen as a promising strategy to stimulate the patient's immune system in combating cancer cells. Nevertheless, the effectiveness of immunotherapy in individuals with gastric cancer (GC) is not yet optimal. Thus, it is crucial to discover biomarkers capable appof predicting the advantages of immunotherapy for tailored treatment. The tumor microenvironment (TME) and its constituents, including cancer-associated fibroblasts (CAFs), exert a substantial influence on immune responses and treatment outcomes. In this investigation, we utilized single-cell RNA sequencing to profile CAFs in GC and established a scoring method, referred to as the CAF score (CAFS), for the prediction of patient prognosis and response to immunotherapy. Through our analysis, we successfully identified distinct subgroups within CAFs based on CAF score (CAFS), namely CAFS-high and CAFS-low subgroups. Notably, we noted that individuals within the CAFS-high subgroup experienced a lessF favorable prognosis and displayed diminished responsiveness to immunotherapy in contrast to the CAFS low subgroup. Furthermore, we analyzed the mutation and immune characteristics of these subgroups, identifying differentially mutated genes and immune cell compositions. We established that CAFS could forecast treatment advantages in patients with gastric cancer, both for chemotherapy and immunotherapy. Its efficacy was additionally confirmed in contrast to other biomarkers, including Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenotypic Score (IPS). These findings emphasize the clinical relevance and potential utility of CAFS in guiding personalized treatment strategies for gastric cancer.

Comprehensive pan-cancer analysis reveals EPHB2 is a novel predictive biomarker for prognosis and immunotherapy response

PURPOSE

Recent studies have increasingly linked Ephrin receptor B2 (EPHB2) to cancer progression. However, comprehensive investigations into the immunological roles and prognostic significance of EPHB2 across various cancers remain lacking.

METHODS

We employed various databases and bioinformatics tools to investigate the impact of EPHB2 on prognosis, immune infiltration, genome instability, and response to immunotherapy. Validation of the correlation between EPHB2 expression and M2 macrophages included analyses using bulk and single-cell transcriptomic datasets, spatial transcriptomics, and multi-fluorescence staining. Moreover, we performed cMap web tool to screen for EPHB2-targeted compounds and assessed their potential through molecular docking and dynamics simulations. Additionally, in vitro validation using lung adenocarcinoma (LUAD) cell lines was conducted to confirm the bioinformatics predictions about EPHB2.

RESULTS

EPHB2 dysregulation was observed across multiple cancer types, where it demonstrated significant diagnostic and prognostic value. Gene Set Enrichment Analysis (GSEA) indicated that EPHB2 is involved in enhancing cellular proliferation, invasiveness of cancer cells, and modulation of the anti-cancer immune response. Furthermore, it is emerged as a pan-cancer marker for M2 macrophage infiltration, supported by integrated analyses of transcriptomics and multiple fluorescence staining. In LUAD cells, knockdown of EPHB2 expression led to a decrease in both cell proliferation and migratory activity.

CONCLUSION

EPHB2 expression may serve as a pivotal indicator of M2 macrophage infiltration, offering vital insights into tumor dynamics and progression across various cancers, including lung adenocarcinoma, highlighting its significant prognostic and therapeutic potential for further exploration.

APOL6 predicts immunotherapy efficacy of bladder cancer by ferroptosis

BACKGROUND

Immune checkpoint inhibitors (ICIs) are rapidly evolving in the management of bladder cancer (BLCA). Nevertheless, effective biomarkers for predicting immunotherapeutic outcomes in BLCA are still insufficient. Ferroptosis, a form of immunogenic cell death, has been found to enhance patient sensitivity to ICIs. However, the underlying mechanisms of ferroptosis in promoting immunotherapy efficacy in BLCA remain obscure.

METHODS

Our analysis of The Cancer Genome Atlas (TCGA) mRNA data using single sample Gene Set Enrichment Analysis (ssGSEA) revealed two immunologically distinct subtypes. Based on these subtypes and various other public cohorts, we identified Apolipoprotein L6 (APOL6) as a biomarker predicting the efficacy of ICIs and explored its immunological correlation and predictive value for treatment. Furthermore, the role of APOL6 in promoting ferroptosis and its mechanism in regulating this process were experimentally validated.

RESULTS

The results indicate that APOL6 has significant immunological relevance and is indicative of immunologically hot tumors in BLCA and many other cancers. APOL6, interacting with acyl-coenzyme A synthetase long-chain family member 4 (ACSL4), mediates immunotherapy efficacy by ferroptosis. Additionally, APOL6 is regulated by signal transducer and activator of transcription 1 (STAT1).

CONCLUSIONS

To conclude, our findings indicate APOL6 has potential as a predictive biomarker for immunotherapy treatment success estimation and reveal the STAT1/APOL6/GPX4 axis as a critical regulatory mechanism in BLCA.

Thoracic sarcopenia was a poor prognostic predictor in patients receiving immunotherapy retain-->for advanced non-small-cell retain-->lung cancer

RATIONALE AND OBJECTIVES

Sarcopenia, as measured at the level of the third lumbar (L3) has been shown to predict the survival of cancer patients. However, many patients with advanced non-small cell lung cancer (NSCLC) do not undergo routine abdominal imaging. The objective of this study was to investigate the association of thoracic sarcopenia with survival outcomes among patients who underwent immunotherapy for NSCLC.

MATERIALS AND METHODS

In this retrospective study, patients who initiated immunotherapy for advanced NSCLC from 2019 to 2022 were enrolled. and detailed patient data were collected. Cross sectional skeletal muscle area was calculated at the fifth thoracic vertebra (T5) on pretreatment chest computed tomography (CT) scan. Gender-specific lowest quartile values was used to define sarcopenia. The risk factors were analyzed using Cox analyses. The log-rank test and the random survival forest (RSF) were used to compare progression free survival (PFS). The model's performance was assessed using calibration curve and the receiver operating characteristic curve (ROC).

RESULTS

A total of 242 patients was included (discovery cohort n = 194, validation cohort n = 48). In the discovery cohort, patients with sarcopenia exhibited significantly poorer PFS (p < 0.001) than patients without sarcopenia. Univariate cox regression revealed that sarcopenia, lung cancer stage, body mass index, smoking status, and neutrophil-to-lymphocyte ratio were predictors of poor PFS. A RSF model was constructed based on the aforementioned parameters, to evaluate the model's efficacy, the ROC curve was utilized. with an area under the curve for predicting 6-month PFS of 0.68 and for 12-month PFS of 0.69. The prediction models for survival outcomes built by the discovery cohort showed similar performance in the validation cohort.

CONCLUSION

Sarcopenia at T5 is independent prognostic factors in patients who received immunotherapy for advanced NSCLC.

Pan-cancer single-cell dissection reveals phenotypically distinct B cell subtypes

Characterizing the compositional and phenotypic characteristics of tumor-infiltrating B cells (TIBs) is important for advancing our understanding of their role in cancer development. Here, we establish a comprehensive resource of human B cells by integrating single-cell RNA sequencing data of B cells from 649 patients across 19 major cancer types. We demonstrate substantial heterogeneity in their total abundance and subtype composition and observe immunoglobulin G (IgG)-skewness of antibody-secreting cell isotypes. Moreover, we identify stress-response memory B cells and tumor-associated atypical B cells (TAABs), two tumor-enriched subpopulations with prognostic potential, shared in a pan-cancer manner. In particular, TAABs, characterized by a high clonal expansion level and proliferative capacity as well as by close interactions with activated CD4 T cells in tumors, are predictive of immunotherapy response. Our integrative resource depicts distinct clinically relevant TIB subsets, laying a foundation for further exploration of functional commonality and diversity of B cells in cancer.

Multimodal analysis unveils tumor microenvironment heterogeneity linked to immune activity and evasion

The cellular and molecular heterogeneity of tumors is a major obstacle to cancer immunotherapy. Here, we use a systems biology approach to derive a signature of the main sources of heterogeneity in the tumor microenvironment (TME) from lung cancer transcriptomics. We demonstrate that this signature, which we called iHet, is conserved in different cancers and associated with antitumor immunity. Through analysis of single-cell and spatial transcriptomics data, we trace back the cellular origin of the variability explaining the iHet signature. Finally, we demonstrate that iHet has predictive value for cancer immunotherapy, which can be further improved by disentangling three major determinants of anticancer immune responses: activity of immune cells, immune infiltration or exclusion, and cancer-cell foreignness. This work shows how transcriptomics data can be integrated to derive a holistic representation of the phenotypic heterogeneity of the TME and to predict its unfolding and fate during immunotherapy with immune checkpoint blockers.

A pan-cancer cuproptosis signature predicting immunotherapy response and prognosis

Background

Cuproptosis may represent a potential biomarker for predicting prognosis and immunotherapy response, but the available evidence is insufficient.

Methods

The multiple single-cell RNA sequencing (scRNA-seq) datasets were analyzed to investigate the specific occurrence of cuproptosis in distinct cell populations. Utilizing 28 scRNA-seq datasets, TCGA pan-cancer cohort, and 10 immunotherapy cohorts, we developed a cuproptosis signature (Cup.Sig). This signature was used to construct prediction models for immunotherapy response and identify potential prognostic biomarkers for pan-cancer using 11 different machine learning algorithms.

Results

Malignant cells demonstrate the higher cuproptosis scores in comparison to other cell types across diverse cancer types. The Cup.Sig exhibits significant associations with cancer hallmarks and immune cell response in multiple cancer types. Leveraging the Cup.Sig, the robust pan-cancer immunotherapy prediction model and prognostic biomarker have been established and validated using diverse datasets from various platforms.

Conclusions

We developed a pan-cancer cuproptosis signature for predicting survival and immunotherapy response.

Spatially Informed Gene Signatures for Response to Immunotherapy in Melanoma

PURPOSE

We aim to improve the prediction of response or resistance to immunotherapies in patients with melanoma. This goal is based on the hypothesis that current gene signatures predicting immunotherapy outcomes show only modest accuracy due to the lack of spatial information about cellular functions and molecular processes within tumors and their microenvironment.

EXPERIMENTAL DESIGN

We collected gene expression data spatially from three cellular compartments defined by CD68+ macrophages, CD45+ leukocytes, and S100B+ tumor cells in 55 immunotherapy-treated melanoma specimens using Digital Spatial Profiling-Whole Transcriptome Atlas. We developed a computational pipeline to discover compartment-specific gene signatures and determine if adding spatial information can improve patient stratification.

RESULTS

We achieved robust performance of compartment-specific signatures in predicting the outcome of immune checkpoint inhibitors in the discovery cohort. Of the three signatures, the S100B signature showed the best performance in the validation cohort (N = 45). We also compared our compartment-specific signatures with published bulk signatures and found the S100B tumor spatial signature outperformed previous signatures. Within the eight-gene S100B signature, five genes (PSMB8, TAX1BP3, NOTCH3, LCP2, and NQO1) with positive coefficients predict the response, and three genes (KMT2C, OVCA2, and MGRN1) with negative coefficients predict the resistance to treatment.

CONCLUSIONS

We conclude that the spatially defined compartment signatures utilize tumor and tumor microenvironment-specific information, leading to more accurate prediction of treatment outcome, and thus merit prospective clinical assessment.

An IL-7 fusion protein targeting EDA fibronectin upregulates TCF1 on CD8+ T-cells, preferentially accumulates to neoplastic lesions, and boosts PD-1 blockade

BACKGROUND

Anti-PD-1 antibodies have revolutionized cancer immunotherapy due to their ability to induce long-lasting complete remissions in a proportion of patients. Current research efforts are attempting to identify biomarkers and suitable combination partners to predict or further improve the activity of immune checkpoint inhibitors. Antibody-cytokine fusions are a class of pharmaceuticals that showed the potential to boost the anticancer properties of other immunotherapies. Extradomain A-fibronectin (EDA-FN), which is expressed in most solid and hematological tumors but is virtually undetectable in healthy adult tissues, is an attractive target for the delivery of cytokine at the site of the disease.

METHODS

In this work, we describe the generation and characterization of a novel interleukin-7-based fusion protein targeting EDA-FN termed F8(scDb)-IL7. The product consists of the F8 antibody specific to the alternatively spliced EDA of FN in the single-chain diabody (scDb) format fused to human IL-7.

RESULTS

F8(scDb)-IL7 efficiently stimulates human peripheral blood mononuclear cells in vitro. Moreover, the product significantly increases the expression of T Cell Factor 1 (TCF-1) on CD8+T cells compared with an IL2-fusion protein. TCF-1 has emerged as a pivotal transcription factor that influences the durability and potency of immune responses against tumors. In preclinical cancer models, F8(scDb)-IL7 demonstrates potent single-agent activity and eradicates sarcoma lesions when combined with anti-PD-1.

CONCLUSIONS

Our results provide the rationale to explore the combination of F8(scDb)-IL7 with anti-PD-1 antibodies for the treatment of patients with cancer.

Expression and Prognostic Relevance of PD-1, PD-L1, and CTLA-4 Immune Checkpoints in Adrenocortical Carcinoma

CONTEXT

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy with poor prognosis in advanced stages. While therapies targeting the checkpoint molecules programmed cell death 1 (PD-1), its ligand PD-L1, and the cytotoxic T lymphocyte-associated protein 4 (CTLA-4) have revolutionized treatment in many cancers, the results in ACCs were heterogeneous.

OBJECTIVE

Their expression in ACC has not been systematically studied and might explain the variable response to immune checkpoint inhibitors.

METHODS

The expression of PD-1, PD-L1 and CTLA-4 was examined in 162 tumor samples from 122 patients with ACC by immunohistochemistry (threshold of >1%) and correlated with tumoral T lymphocyte infiltration and clinical endpoints. Finally, univariate and multivariate analyses of progression-free and overall survival were performed.

RESULTS

PD-1 and PD-L1 were expressed in 26.5% and 24.7% of samples, respectively, with low expression in most tumor samples (median positive cells: 2.1% and 21.7%). In contrast, CTLA-4 expression was observed in 52.5% of ACC with a median of 38.4% positive cells. Positive PD-1 expression was associated with longer progression-free survival (HR 0.50, 95% CI 0.25-0.98, P = .04) even after considering prognostic factors. In contrast, PD-L1 and CTLA-4 did not correlate with clinical outcome. Additionally, PD-1 and PD-L1 expression correlated significantly with the amount of CD3+, CD4+, FoxP3+, and CD8+ T cells.

CONCLUSION

The heterogeneous expression of PD1, PD-L1, and CTLA-4 in this large series of well-annotated ACC samples might explain the heterogeneous results of the immunotherapies in advanced ACC. In addition, PD-1 expression is a strong prognostic biomarker that can easily be applied in routine clinical care and histopathological assessment.

Unveiling major histocompatibility complex-mediated pan-cancer immune features by integrated single-cell and bulk RNA sequencing

Immune checkpoint inhibitors (ICIs) have transformed cancer therapy, yet persistent challenges such as low response rate and significant heterogeneity necessitate attention. The pivotal role of the major histocompatibility complex (MHC) in ICI efficacy, its intricate impacts and potentials as a prognostic marker, warrants comprehensive exploration. This study integrates single-cell RNA sequencing (scRNA-seq), bulk RNA-seq, and spatial transcriptomic analyses to unveil pan-cancer immune characteristics governed by the MHC transcriptional feature (MHC.sig). Developed through scRNA-seq analysis of 663,760 cells across diverse cohorts and validated in 30 solid cancer types, the MHC.sig demonstrates a robust correlation between immune-related genes and infiltrating immune cells, highlighting its potential as a universal pan-cancer marker for anti-tumor immunity. Screening the MHC.sig for therapeutic targets using CRISPR data identifies potential genes for immune therapy synergy and validates its predictive efficacy for ICIs responsiveness across diverse datasets and cancer types. Finally, analysis of cellular communication patterns reveals interactions between C1QC+macrophages and malignant cells, providing insights into potential therapeutic agents and their sensitivity characteristics. This comprehensive analysis positions the MHC.sig as a promising marker for predicting immune therapy outcomes and guiding combinatorial therapeutic strategies.

Tumor-associated macrophage clusters linked to immunotherapy in a pan-cancer census

Transcriptional heterogeneity of tumor-associated macrophages (TAMs) has been investigated in individual cancers, but the extent to which these states transcend tumor types and represent a general feature of cancer remains unclear. We performed pan-cancer single-cell RNA sequencing analysis across nine cancer types and identified distinct monocyte/TAM composition patterns. Using spatial analysis from clinical study tissues, we assessed TAM functions in shaping the tumor microenvironment (TME) and influencing immunotherapy. Two specific TAM clusters (pro-inflammatory and pro-tumor) and four TME subtypes showed distinct immunological features, genomic profiles, immunotherapy responses, and cancer prognosis. Pro-inflammatory TAMs resided in immune-enriched niches with exhausted CD8+ T cells, while pro-tumor TAMs were restricted to niches associated with a T-cell-excluded phenotype and hypoxia. We developed a machine learning model to predict immune checkpoint blockade response by integrating TAMs and clinical data. Our study comprehensively characterizes the common features of TAMs and highlights their interaction with the TME.

Blockade of LAG-3 and PD-1 leads to co-expression of cytotoxic and exhaustion gene modules in CD8+ T cells to promote antitumor immunity

Relatlimab (rela; anti-LAG-3) plus nivolumab (nivo; anti-PD-1) is safe and effective for treatment of advanced melanoma. We designed a trial (NCT03743766) where advanced melanoma patients received rela, nivo, or rela+nivo to interrogate the immunologic mechanisms of rela+nivo. Analysis of biospecimens from this ongoing trial demonstrated that rela+nivo led to enhanced capacity for CD8+ T cell receptor signaling and altered CD8+ T cell differentiation, leading to heightened cytotoxicity despite the retention of an exhaustion profile. Co-expression of cytotoxic and exhaustion signatures was driven by PRDM1, BATF, ETV7, and TOX. Effector function was upregulated in clonally expanded CD8+ T cells that emerged after rela+nivo. A rela+nivo intratumoral CD8+ T cell signature was associated with a favorable prognosis. This intratumoral rela+nivo signature was validated in peripheral blood as an elevated frequency of CD38+TIM3+CD8+ T cells. Overall, we demonstrated that cytotoxicity can be enhanced despite the retention of exhaustion signatures, which will inform future therapeutic strategies.

IRnet: Immunotherapy response prediction using pathway knowledge-informed graph neural network

INTRODUCTION

Immune checkpoint inhibitors (ICIs) are potent and precise therapies for various cancer types, significantly improving survival rates in patients who respond positively to them. However, only a minority of patients benefit from ICI treatments.

OBJECTIVES

Identifying ICI responders before treatment could greatly conserve medical resources, minimize potential drug side effects, and expedite the search for alternative therapies. Our goal is to introduce a novel deep-learning method to predict ICI treatment responses in cancer patients.

METHODS

The proposed deep-learning framework leverages graph neural network and biological pathway knowledge. We trained and tested our method using ICI-treated patients' data from several clinical trials covering melanoma, gastric cancer, and bladder cancer.

RESULTS

Our results demonstrate that this predictive model outperforms current state-of-the-art methods and tumor microenvironment-based predictors. Additionally, the model quantifies the importance of pathways, pathway interactions, and genes in its predictions. A web server for IRnet has been developed and deployed, providing broad accessibility to users at https://irnet.missouri.edu.

CONCLUSION

IRnet is a competitive tool for predicting patient responses to immunotherapy, specifically ICIs. Its interpretability also offers valuable insights into the mechanisms underlying ICI treatments.

A Novel Gene Signature Associated with Protein Post-translational Modification to Predict Clinical Outcomes and Therapeutic Responses of Colorectal Cancer

Accumulated evidence highlights the biological significance of diverse protein post-translational modifications (PTMs) in tumorigenicity and progression of colorectal cancer (CRC). In this study, ten PTM patterns (ubiquitination, methylation, phosphorylation, glycosylation, acetylation, SUMOylation, citrullination, neddylation, palmitoylation, and ADP-ribosylation) were analyzed for model construction. A post-translational modification index (PTMI) with a 14-gene signature was established. CRC patients with high PTMI had a worse prognosis after validating in nine independent datasets. By incorporating PTMI with clinical features, a nomogram with excellent predictive performance was constructed. Two molecular subtypes of CRC with obvious difference in survival time were identified by unsupervised clustering. Furthermore, PTMI was related to known immunoregulators and key tumor microenvironment components. Low-PTMI patients responded better to fluorouracil-based chemotherapy and immune checkpoint blockade therapy compared to high-PTMI patients, which was validated in multiple independent datasets. However, patients with high PTMI might be sensitive to bevacizumab. In short, we established a novel PTMI model by comprehensively analyzing diverse post-translational modification patterns, which can accurately predict clinical prognosis and treatment response of CRC patients.

Comprehensive analysis of single cell and bulk RNA sequencing reveals the heterogeneity of melanoma tumor microenvironment and predicts the response of immunotherapy

BACKGROUND

Tumor microenvironment (TME) heterogeneity is an important factor affecting the treatment response of immune checkpoint inhibitors (ICI). However, the TME heterogeneity of melanoma is still widely characterized.

METHODS

We downloaded the single-cell sequencing data sets of two melanoma patients from the GEO database, and used the "Scissor" algorithm and the "BayesPrism" algorithm to comprehensively analyze the characteristics of microenvironment cells based on single-cell and bulk RNA-seq data. The prediction model of immunotherapy response was constructed by machine learning and verified in three cohorts of GEO database.

RESULTS

We identified seven cell types. In the Scissor+ subtype cell population, the top three were T cells, B cells and melanoma cells. In the Scissor- subtype, there are more macrophages. By quantifying the characteristics of TME, significant differences in B cells between responders and non-responders were observed. The higher the proportion of B cells, the better the prognosis. At the same time, macrophages in the non-responsive group increased significantly. Finally, nine gene features for predicting ICI response were constructed, and their predictive performance was superior in three external validation groups.

CONCLUSION

Our study revealed the heterogeneity of melanoma TME and found a new predictive biomarker, which provided theoretical support and new insights for precise immunotherapy of melanoma patients.

The Tumor Microbiome as a Predictor of Outcomes in Patients with Metastatic Melanoma Treated with Immune Checkpoint Inhibitors

Emerging evidence supports the important role of the tumor microbiome in oncogenesis, cancer immune phenotype, cancer progression, and treatment outcomes in many malignancies. In this study, we investigated the metastatic melanoma tumor microbiome and its potential roles in association with clinical outcomes, such as survival, in patients with metastatic disease treated with immune checkpoint inhibitors (ICI). Baseline tumor samples were collected from 71 patients with metastatic melanoma before treatment with ICIs. Bulk RNA sequencing (RNA-seq) was conducted on the formalin-fixed, paraffin-embedded and fresh frozen tumor samples. Durable clinical benefit (primary clinical endpoint) following ICIs was defined as overall survival >24 months and no change to the primary drug regimen (responders). We processed RNA-seq reads to carefully identify exogenous sequences using the {exotic} tool. The age of the 71 patients with metastatic melanoma ranged from 24 to 83 years, 59% were male, and 55% survived >24 months following the initiation of ICI treatment. Exogenous taxa were identified in the tumor RNA-seq, including bacteria, fungi, and viruses. We found differences in gene expression and microbe abundances in immunotherapy-responsive versus nonresponsive tumors. Responders showed significant enrichment of bacteriophages in the phylum Uroviricota, and nonresponders showed enrichment of several bacteria, including Campylobacter jejuni. These microbes correlated with immune-related gene expression signatures. Finally, we found that models for predicting prolonged survival with immunotherapy using both microbe abundances and gene expression outperformed models using either dataset alone. Our findings warrant further investigation and potentially support therapeutic strategies to modify the tumor microbiome in order to improve treatment outcomes with ICIs.

SIGNIFICANCE

We analyzed the tumor microbiome and interactions with genes and pathways in metastatic melanoma treated with immunotherapy and identified several microbes associated with immunotherapy response and immune-related gene expression signatures. Machine learning models that combined microbe abundances and gene expression outperformed models using either dataset alone in predicting immunotherapy responses.

Mapping the single cell spatial immune landscapes of the melanoma microenvironment

Melanoma is a highly immunogenic malignancy with an elevated mutational burden, diffuse lymphocytic infiltration, and one of the highest response rates to immune checkpoint inhibitors (ICIs). However, over half of all late-stage patients treated with ICIs will either not respond or develop progressive disease. Spatial imaging technologies are being increasingly used to study the melanoma tumor microenvironment (TME). The goal of such studies is to understand the complex interplay between the stroma, melanoma cells, and immune cell-types as well as their association with treatment response. Investigators seeking a better understanding of the role of cell location within the TME and the importance of spatial expression of biomarkers are increasingly turning to highly multiplexed imaging approaches to more accurately measure immune infiltration as well as to quantify receptor-ligand interactions (such as PD-1 and PD-L1) and cell-cell contacts. CyTOF-IMC (Cytometry by Time of Flight - Imaging Mass Cytometry) has enabled high-dimensional profiling of melanomas, allowing researchers to identify complex cellular subpopulations and immune cell interactions with unprecedented resolution. Other spatial imaging technologies, such as multiplexed immunofluorescence and spatial transcriptomics, have revealed distinct patterns of immune cell infiltration, highlighting the importance of spatial relationships, and their impact in modulating immunotherapy responses. Overall, spatial imaging technologies are just beginning to transform our understanding of melanoma biology, providing new avenues for biomarker discovery and therapeutic development. These technologies hold great promise for advancing personalized medicine to improve patient outcomes in melanoma and other solid malignancies.